Method for generating fat distribution image by CT system

ABSTRACT

A method of generating an image representing fat distributions, comprising the steps of scanning two different levels of tube voltage using a phantom containing a sample rod of a fat standard material and a plurality of sample rods with different densities of a bone mineral equivalent material, to generate two cross sectional image data; detecting the CT number of each pixel in an entire region or an objective region of the cross sectional image data as the CT number of a tissue including fat (αwf); detecting the CT number of the bone mineral equivalent material to calculate a linear regression between the CT number and the density of the bone mineral equivalent material and to define the CT number of a tissue excluding fat (αnf); detecting the CT number of the fat standard material (αff), while detecting the CT number of a soft tissue standard material (αst), wherein individual CT numbers are applied to the equation 
     
         αwf=αnf+β·(αff-αst) 
    
     with β defined as a fat ratio parameter; calculating the fat ratio parameter β of each pixel in the entire region or an objective region of the cross sectional image data using the foregoing equation, with attention focused on the finding that the density of the bone mineral equivalent material is constant at scanning at any different level of tube voltage, and finally generating an image based on the fat ratio parameter β.

TECHNICAL FIELD

The present invention relates to a method for generating a fatdistribution image by a CT system. More particularly, the presentinvention relates to a method for generating a fat distribution image bya CT system, wherein an image of fat distribution is generated from across sectional image data via X ray.

BACKGROUND ART

As the method for generating a fat distribution image from a crosssectional image data via X ray, a method is known comprising determininga threshold value so as to extract the CT number range from -130 to -100from the cross sectional image data, based on the finding that the CTnumber of fat is about -130 to -100.

However, because the fat composition ratio in human bodies variesdepending on the tissue, the conventional method described abovecomprising determining a threshold value has problems in that fat cannotbe separated solely from other components in tissues and in that the fatcomposition ratio in ROI (region of interest) cannot be determined.

DISCLOSURE OF INVENTION

Thus, the object of the present invention resides in providing a methodfor generating a fat distribution image by a CT system, wherein an imagerepresenting fat distribution is generated, based on the fat compositionratio in a tissue.

In a first aspect of the present invention, the method for generating afat distribution image by a CT system wherein an image of fatdistribution is generated from a cross sectional image data via X raycomprises the steps of a scanning step wherein scanning is effected atleast at two different levels of X-ray tube voltage using a phantomcontaining a sample rod of a fat standard material in addition to pluralsample rods with different densities of a bone mineral equivalentmaterial to generate at least two cross sectional image data, a CTnumber detecting step wherein the CT number of each pixel in the wholeregion or an objective region of the cross sectional image data isdetected as the "CT number of a tissue including fat", ie. αwf, a linearregression calculating step wherein the CT number of the bone mineralequivalent material is detected in the cross sectional image datafollowing the principle of quantitative measurement of bone mineral massto calculate a linear regression between the CT number and the densityof the bone mineral equivalent material and to define the CT number asthe "CT number of a tissue excluding fat", ie. αnf, a fat standardmaterial CT number detecting step wherein the "CT number of the fatstandard material", ie. αff is detected in the cross sectional imagedata, a soft tissue standard material CT number detecting step whereinthe CT number of blood in the cross sectional image data is detected asthe "CT number of a soft tissue standard material", ie. αst, a CT numberapplication step wherein "α" in the equation α=αwf-αnf is defined as thevariation of the CT number due to fat while "β" in the equationα=β·{αff-αst} is defined as a fat ratio parameter and the "CT number ofa tissue including fat" ie. αwf, etc., should be applied to the equation

    αwf=αnf+β·{αff-αst}  Equation (A),

a fat ratio parameter calculating step wherein the density of the bonemineral equivalent material and the "CT number of a tissue excludingfat", ie. αnf should be deleted in at least two linear regressionscalculated at the linear regression calculating step and in at least twoequations (A) to be applied at the CT number application step, tocalculate the fat ratio parameter β on the basis of the detected CTnumber of each pixel at the CT number detecting step, and a fat ratioparameter image generation step wherein an image is generated on thebasis of the calculated fat ratio parameter β.

According to the method for generating a fat distribution image by a CTsystem of the present invention, scanning is effected at least at twodifferent levels of X-ray tube voltage at the scanning step, using aphantom containing a sample rod of a fat standard material in additionto plural sample rods with different densities of a bone mineralequivalent material to generate at least two cross sectional image data.

At the CT number detecting step, the CT number of each pixel in thewhole region or an objective region of the cross sectional image data isdetected as the "CT number of a tissue including fat", ie. αwf.

At the linear regression calculating step, the CT number of the bonemineral equivalent material is detected in the cross sectional imagedata following the principle of quantitative measurement of bone mineralmass to calculate a linear regression between the CT number and thedensity of the bone mineral equivalent material. Then, the CT number isdesignated the "CT number of a tissue excluding fat", ie. αnf.Furthermore, at least two linear regressions should be calculated,depending on the scanning number at different levels of tube voltage.

At the fat standard material CT number detecting step, the "CT number ofthe fat standard material", ie. αff is detected in the cross sectionalimage data, and at the soft tissue standard material CT number detectingstep, the CT number of blood in the cross sectional image data isdetected as the "CT number of a soft tissue standard material", ie. αst.

At the CT number application step, "β" in the equation α=β·{αff-αst} isdefined as a fat ratio parameter while "a" in the equation α=αwf-αnf isdefined as the variation of the CT number due to fat, and the "CT numberof a tissue including fat" ie. αwf, etc. should be applied to theequation

    αwf=αnf+β·{αff-αst}  Equation (A).

The above equation (A) is known, so no detailed explanation thereof willnow be described.

At the fat ratio parameter calculating step with attention focused onthe finding that the density of the bone mineral equivalent materialshould be constant at scanning at any different level of X-ray tubevoltage, the density of the bone mineral equivalent material and the "CTnumber of a tissue excluding fat", ie. αnf should be deleted in at leasttwo linear regressions calculated at the linear regression calculatingstep and in at least two equations (A) to be applied from the CT numberapplication step, to calculate the fat ratio parameter β on the basis ofthe detected CT number of each pixel at the CT number detecting step.

At the fat ratio parameter image generation step, an image is generatedon the basis of the calculated fat ratio parameter β. Because the fatratio parameter "β" is a parameter representing the fat compositionratio in tissues, the generated image displays the distribution of fat(composition ratio).

In a second aspect of the present invention, the method for generating afat distribution image by a CT system comprises a soft tissue standardmaterial CT number detecting step wherein use is made of a phantomcontaining a sample rod of a fat standard material and a sample rod of awater equivalent material in addition to plural sample rods with variousdensities of a bone mineral equivalent material to detect the CT numberof the water equivalent material in the cross sectional image datagenerated from scanning as the "CT number of a soft tissue standardmaterial", ie. αst instead of the soft tissue standard material CTnumber detecting step wherein the CT number of blood in the crosssectional image data is detected as the "CT number of a soft tissuestandard material", ie. αst.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart depicting the procedures of the method ofgenerating a fat distribution image in accordance with the presentinvention;

FIG. 2 is an illustration of a phantom to be used in the presentinvention;

FIG. 3 is an illustration of a cross sectional image in accordance withthe present invention;

FIG. 4 is an illustration of a linear regression in accordance with thepresent invention; and

FIG. 5 is a block diagram of a CT system for carrying out the method forgenerating a fat distribution image in accordance with the presentinvention.

BEST MODE FOR CARRYING OUT THE INVENTION

The present invention will now be explained hereinbelow in examples asshown in figures. But the present invention is not limited to theexamples.

FIG. 5 is a block diagram of CT system 1 for carrying out the method forgenerating a fat distribution image in accordance with the presentinvention.

X-ray tube 3 and detector 4, both placed in gantry 2, are integrallyrotated with gantry rotation system 7.

The detector 4 detects the intensity of X-ray transmitted throughsubject K.

X-ray generation control circuit 5 is connected to x-ray tube 3, tocontrol X-ray generation and the cessation thereof.

Detector control circuit 6 controls the timing to operate detector 4.

Table 8 is for placing the subject K, and is linearly movable with tablemovable unit 9.

Data collection unit 10 collects projection data from the detector 4.

Image reconstitution unit 11 reconstitutes an image based on theprojection data from the data collection unit 10, to output crosssectional image data.

On the basis of the cross sectional image data obtained by the imagereconstitution unit 11, image generation unit 12 is for carrying out theprocedures of the present invention as described hereinafter, to outputnew cross sectional image data.

Data storage unit 13 stores the cross sectional image data.

Display unit 14 displays a cross sectional image on the basis of thecross sectional image data from the image reconstitution unit 11 and theimage generation unit 12.

System control unit 20 transfers and receives necessary signals to andfrom X-ray generation control circuit 5, detector control circuit 6,gantry rotation unit 7, table movable unit 9, data collection unit 10,image reconstitution unit 11, image generation unit 12, data storageunit 13, and display unit 14.

Operation unit 30 is for an operator to input commands and the like.

FIG. 2 is an illustrative figure of a phantom to be used for the methodfor generating a fat distribution image in one example in accordancewith the present invention.

The phantom P is a phantom to be used for quantitative BMD (bone mineraldensity) measurement and is capable of containing plural sample rods S1,S2, . . . with various densities of a bone mineral equivalent material.The bone mineral equivalent material is, for example, potassium hydrogenphosphate, calcium carbonate and the like.

In this example, the phantom P also contains sample rod Sf of a fatstandard material, in addition to the sample rods S1, . . . , S4 withvarious densities of the bone mineral equivalent material.

FIG. 1 is a flow chart representing the method for generating a fatdistribution image in one example in accordance with the presentinvention. Following the flow chart of FIG. 1, explanation will now bemade.

Following the principle of quantitative BMD measurement, phantom P isplaced below the waist of subject K. Then, after the third lumbarvertebrae is determined as a scanning cross section, for example, thefollowing procedures will be carried out when an operator directs tocommence the generation of a fat distribution image through operationunit 30.

At step D1, a first scanning of the subject K and the phantom P iseffected at a X-ray tube voltage of E1 (kV).

At step D2, the CT number of each pixel in the cross sectional imagedata from the first scanning is designated the CT number of a tissueincluding fat, ie. αwfl (wf=with fat). The αwfl should be expressed asαwfli when the pixel number is "i", but the "i" is neglected so as tosimplify the explanation.

FIG. 3 shows a schematic view of the cross sectional image from thecross sectional image data. "H" represents contour; and "B" representsthird lumbar vertebrae.

At step D3, the CT numbers of the sample rods, S1, . . . , S4 withvarious densities of the bone mineral equivalent material in the crosssectional image data are detected, to calculate a linear regression byleast squares method, as shown in FIG. 4 and represented by thefollowing equation;

    y=G·x+C                                           (b)

wherein X axis represents BMD and y axis represents CT number.

Because the sample rods S1, . . . , S4 with various densities of thebone mineral equivalent material do not contain fat, the CT number on yaxis is defined as the "CT number of a tissue excluding fat", ie. αnf(nf=no fat), and the linear regression from the first scanning is nowrepresented as follows;

    αnf1=G1·x1+C1                               (b1)

At step D4, the CT number of sample rod Sf of a fat standard material,ie. αff1 (ff=full fat) is detected in the cross sectional image data.

At step D5, the CT number of aorta in the cross sectional image data isdetected and defined as the CT number of a soft tissue standardmaterial. ie. αst1 (st=soft tissue).

Any tissue on the cross sectional image (data) includes fat. When the CTnumber of a tissue as ROI is detected and represented as αwf (wf=withfat), the following equation is known;

    αwf=αnf+β·{αff-αst}  (A)

wherein β is a fat ratio parameter representing the fat compositionratio in a tissue.

At step D6, each CT number detected at the first scanning is applied tothe following equation as in the above equation (A);

    αwf1=αnf1+β·{αff1-αst1}(A1)

At step D7, then, a second scanning is effected on the subject K and thephantom P at a X-ray tube voltage of E2(kV).

At step D8, the CT number of each pixel in the cross sectional imagedata from the second scanning is designated the CT number of a tissueincluding fat, ie. αwf2. The αwf2 should be expressed as αwf2i when thepixel number is "i" but the "i" is neglected so as to simplify theexplanation.

At step D9, the CT numbers of the sample rods, S1, . . . , S4 withvarious densities of the bone mineral equivalent material are detectedin the cross sectional image data to calculate a linear regression byleast squares method, as shown in FIG. 4. Following the same manner asin the above step D3, the linear regression from the second scanning isrepresented by the following equation;

    αnf2=G2·x2+C2                               (b2)

At step D10, the CT number of the sample rod Sf of the fat standardmaterial, ie. αff2 is detected in the cross sectional image data.

At step D11, the CT number of aorta is detected in the cross sectionalimage data and defined as the CT number of a soft tissue standardmaterial. ie. αst2.

At step D12, each CT number detected from the second scanning is appliedto the following equation as in the above equation (A);

    αwf2=αnf2+β·{αff2-αst2}(A2)

At step D13, because BMD is constant at scanning at any different X-raytube voltage so "x" is deleted in the equations (b1) and (b2) providedthat x1=x2=x, the fat ratio parameter β is calculated on the basis ofthe equations (A1) and (A2) by the following equation;

    β=[G2{αwf1-C1}-G1{αwf2-C2}]/ [G2{αff1-αst1}-G1{αff2-αst2}].

As has been described above, because the pixel number "i" is neglectedin the expression αwf1 and αwf2 for simplifying explanation (in otherwords, in the above equations, β should be expressed as βi; and αwf1 andαwf2 should be expressed as αwf1i and αwf2i, respectively), the step 13is carried out on any pixel number "i" corresponding to the fatdistribution image to be displayed.

At step D14, based on the calculated fat ratio parameter β of eachpixel, display unit 14 displays an image of an intensity and a gradationcorresponding to the dimension of the fat ratio parameter β.

Because the fat ratio parameter β is a parameter representing the fatcomposition ratio in a tissue, the image described above represents thedistribution of fat (composition ratio).

In the above example, the CT number of aorta in the cross sectionalimage data is adopted as the CT number of a soft tissue standardmaterial, ie. αst, but use may be made of the CT number of a waterequivalent material in the form of a sample rod placed in the phantom pin place of the CT number of aorta.

According to the method for generating a fat distribution image by a CTsystem, an image representing fat distribution can be generated, basedon the fat composition ratio in a tissue.

What is claimed is:
 1. A method for generating a fat distribution image by a CT system wherein an image of fat distribution is generated from a cross sectional image data via X ray, comprising the steps of a scanning step wherein scanning is effected at least at two different levels of X-ray tube voltage using a phantom containing a sample rod of a fat standard material in addition to plural sample rods with different densities of a bone mineral equivalent material to generate at least two cross sectional image data, a CT number detecting step wherein the CT number of each pixel in the whole region or an objective region of the cross sectional image data is detected as the "CT number of a tissue including fat", ie. αwf, a linear regression calculating step wherein the CT number of the bone mineral equivalent material is detected in the cross sectional image data following the principle of quantitative measurement of bone mineral mass to calculate a linear regression between the CT number and the density of the bone mineral equivalent material and to define the CT number as the "CT number of a tissue excluding fat", ie. αnf, a fat standard material CT number detecting step wherein the "CT number of the fat standard material", ie. αff is detected in the cross sectional image data, a soft tissue standard material CT number detecting step wherein the CT number of blood in the cross sectional image data is detected as the "CT number of a soft tissue standard material", ie. αst, a CT number application step wherein "α" in the equation α=αwf-αnf is defined as the variation of the CT number due to fat while "β" in the equation α=·{αff-αst} is defined as a fat ratio parameter and the "CT number of a tissue including fat" ie. αwf, etc., is applied to the equation

    αwf=αnf+β·{αff-αst}  Equation (A)

a fat ratio parameter calculating step wherein the density of the bone mineral equivalent material and the "CT number of a tissue excluding fat", ie. αnf is deleted in at least two linear regressions calculated at the linear regression calculating step and in at least two equations (A) to be applied at the CT number application step, to calculate the fat ratio parameter β on the basis of the detected CT number of each pixel at the CT number detecting step, and a fat ratio parameter image generation step wherein an image is generated on the basis of the calculated fat ratio parameter β.
 2. A method for generating a fat distribution image by a CT system wherein an image of fat distribution is generated from a cross sectional image data via X ray, comprising the steps of a scanning step wherein scanning is effected at least at two different levels of X-ray tube voltage using a phantom containing a sample rod of a fat standard material and a sample rod of a water equivalent material in addition to plural sample rods with different densities of a bone mineral equivalent material to generate at least two cross sectional image data, a CT number detecting step wherein the CT number of each pixel in the whole region or an objective region of the cross sectional image data is detected as the "CT number of a tissue including fat", ie. αwf, a linear regression calculating step wherein the CT number of the bone mineral equivalent material is detected in the cross sectional image data following the principle of quantitative measurement of bone mineral mass to calculate a linear regression between the CT number and the density of the bone mineral equivalent material and to define the CT number as the "CT number of a tissue excluding fat", ie. αnf, a fat standard material CT number detecting step wherein the "CT number of the fat standard material", ie. αff is detected in the cross sectional image data, a soft tissue standard material CT number detecting step wherein the CT number of the water equivalent material in the cross sectional image data is detected as the "CT number of a soft tissue standard material", ie. αst, a CT number application step wherein "α" in the equation α=αwf-αnf is defined as the variation of the CT number due to fat while "β" in the equation α=β·{αff-αst} is defined as a fat ratio parameter and the "CT number of a tissue including fat" ie. αwf, etc., is applied to the equation

    αwf=αnf+β·{αff-αst}  Equation (A)

a fat ratio parameter calculating step wherein the density of the bone mineral equivalent material and the "CT number of a tissue excluding fat", ie. αnf is deleted in at least two linear regressions calculated at the linear regression calculating step and in at least two equations (A) to be applied at the CT number application step, to calculate the fat ratio parameter β on the basis of the detected CT number of each pixel at the CT number detecting step, and a fat ratio parameter image generation step wherein an image is generated on the basis of the calculated fat ratio parameter β. 